Users interact with remote entities for a wide range of purposes. For example, a user may interact with a web server hosting a search engine to receive relevant information regarding a query.
In another illustrative example, a user may interact with a customer support agent or a chatbot to receive information about a product or a service, to pay a bill, to resolve a query, to lodge a complaint and the like.
Typically, question and answer (Q&A) matching platforms facilitate such user interactions, where a user query is matched to an appropriate response from among a finite set of stored responses. The matching is primarily performed, among other criteria, based on a match between the words in the user query and the words in each response.
However, in many scenarios, such an approach is found to be limited in its ability to provide users with satisfactory query responses. For example, in some scenarios, a user query may include some words, which are not present in any of the stored responses. In such a situation, the query responses to be provided to the user are selected by matching remaining words in the user query to those in the stored responses. A provisioning of query responses based on matching of only some words in the query may not adequately serve the user's purpose.
In another example scenario, a single user input may include more than one question. In such a case, matching words in the user input to the stored responses in order to identify appropriate responses may result in selecting responses that may not accurately answer any one of the questions in the user input.
The responses provisioned in this manner can be frustrating to the user and may lead to the user abandoning the interaction.
Therefore, there is a need to improve comprehension of user queries vis-à-vis stored responses and provide substantially improved responses to the users.